First Results From a Calibrated Network of Low‐Cost PM2.5 Monitors in Mombasa, Kenya Show Exceedance of Healthy Guidelines

Abstract The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub‐Saharan Africa. Well calibrated low‐cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node‐S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya's second largest city. As‐reported Clarity Node‐S data from January 2023 through April 2023 was moderately correlated with the TEOM‐1400a measurements (R 2 = 0.61) and exhibited a mean absolute error (MAE) of 7.03 μg m−3. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 μg m−3 respectively. The R 2 value improved to 0.63 for the MLR model but all other models registered a decrease (R 2 = 0.44 and 0.60 respectively). Applying the correction factor to a five‐sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18 μg m−3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.


Introduction
Air pollution poses a considerable threat on world health, with its most pronounced impact felt in low-and middle-income countries (LMICs).Currently ranking fourth among the leading causes of global morbidity and mortality, it closely trails high blood pressure, smoking and unhealthy diets (Hoffmann et al., 2021).The gravity of the situation is underscored by epidemiological studies associating about 6.5 million premature deaths and 6 million preterm births globally each year to air pollution (Ghosh et al., 2021;McDuffie et al., 2021).These statistics highlight the imperative to prioritize interventions that tackle the diverse health risks posed by air pollution.
Fine particulate matter (PM), known as PM 2.5 , stands out as the most hazardous among major air pollutants.These particles are easily respirable and exhibit a propensity to deposit in the pulmonary region based on their size (Dharaiya et al., 2023).Controlling PM pollution is a key focus of national and local government bodies in many countries (e.g., the Environmental Protection Agency in the United States) and is historically measured using certified reference methods, with a high degree of accuracy and precision.Devices fitting this description are • Mean daily PM 2.5 concentrations in Mombasa ranged from 12 to 18 μg m 3 depending on site location and time of year • Daily PM 2.5 concentrations were higher during the dry seasons, early morning and afternoon, and lower during the wet seasons • Sites nearby frequent industrial activity exceeded the WHO daily limits of PM 2.5 more than 50% of the time

Supporting Information:
Supporting Information may be found in the online version of this article.
normally filter-based methods like high volume samplers, though near real time monitoring methods like beta attenuation monitors and tapered element oscillating microbalance (TEOM) are also certified and used in air quality management (Ghamari et al., 2022;Hagan & Kroll, 2020).While these meet most legal requirements, equipping and maintaining air quality stations with such monitors can be a financial burden and often results in relatively sparse monitoring.In a complex urban environment, for instance, a single reference monitor costing more than $10,000 cannot give information about localized variations that are important for protecting health.Depending on deployment characteristics, a single reference monitor may only represent tens or hundreds km 2 by area and inform pollution in highly specific geographies (Alfano et al., 2020;Levy Zamora et al., 2019).
Fortunately, there has been a new paradigm shift in conventional PM monitoring with the advent of low-cost sensor systems.These devices, primarily portable optical particle counters or nephelometers, operate based on the principle of light scattering to infer the PM number concentration, which can then be converted to mass concentration assuming a particle density and shape.Priced between $150 to $3,000, these devices offer a costeffective solution to capture spatiotemporal variability, enabling high-density near real-time air quality monitoring (Feenstra et al., 2019;Zimmerman et al., 2018).Recent work has shown that low-cost air quality sensors, especially when carefully calibrated, can be extremely powerful in revealing air quality levels and sources of air pollution (Amegah, 2018;Giordano et al., 2021;McFarlane, Isevulambire, et al., 2021;McFarlane, Raheja, et al., 2021;Okure et al., 2022;Raheja et al., 2022Raheja et al., , 2023;;Subramanian & Garland, 2021;Westervelt et al., 2024).An outstanding issue remains data quality, though the strengths and weaknesses of these devices have been wellcharacterized recently (Hagan & Kroll, 2020;Jayaratne et al., 2018;Molina Rueda et al., 2023;Ouimette et al., 2022;Tryner et al., 2020).
For LMICs like Kenya, where adequate monitoring and scientific information are lacking, the potential of lowcost sensors cannot be overstated.With only a few reference monitors and a few sensors reporting air quality data, primarily concentrated in the capital, Nairobi, there is a pressing need for comprehensive monitoring in other regions of the country.Previous studies on air quality in Mombasa are few (Simiyu et al., 2018;Yussuf et al., 2023) and have only relied on simulated model output, for example, from the Modern-Era Retrospective analysis for Research and Applications version 2 reanalysis (MERRA-2).This work therefore presents, to our knowledge, the first surface observations of PM 2.5 in the city of Mombasa, the second-largest city in Kenya with a population of about 3.5 million and a major port city, and represents some of the first dedicated air quality research in this area.

Sampling Locations
Mombasa is the second largest city in Kenya and lies on the southeast of the Kenyan coast within coordinates (3°8 0′, 4°10′S and 39°60′, 39°80′E).The city has an area of 295 km 2 with an increasing number of inhabitants at more than 3.5 million (KNBS, 2019).It is arguably the largest port in East Africa and plays a pivotal role in trade in the region.It is home to several manufacturing and processing industries including iron smelting, steel rolling mills, cement mining and oil companies.Mombasa is also an iconic tourist destination with clusters of sandy beaches and World Heritage sites (KPA, 2017).
Despite its economic significance, Mombasa faces understudied environmental challenges, particularly in terms of air quality.Potential anthropogenic sources of pollution include operation of minibuses (Matatus), motorized tricycles (Tuk Tuks), cargo ships, haulage trucks, container handling equipment, thermal power plants, cement factories, and the burning of open and biomass fuels.The combination of industrial activities, transportation, and tourism makes Mombasa a complex urban environment susceptible to air quality issues.

Clarity Node-S
Clarity Node-S (Clarity Movement Co., Berkeley, CA, USA) is a low-cost multipollutant monitor that consists of a Plantower PMS6003, an electrochemical cell sensor (Alphasense), and a Bosche BME280 sensor for the simultaneous measurement of PM, NO 2 , temperature, and relative humidity (Nobell et al., 2023).The Plantower PMS6003 sensors are specifically designed for the measurement of PM and are equipped with two dual lasers that operate alternately, providing continuous cross-verification to ensure sensor longevity (Nobell et al., 2023).When the sensor draws ambient air containing particles of different sizes into its measurement volume, a laser beam  illuminates these particles.The resulting scattered light is then detected perpendicularly by a photodiode detector.Subsequently, the raw light signals undergo filtering and amplification through electronic filters and circuitry before being converted into mass concentrations.According to the manufacturer's data sheet, this particular sensor model has a measurement range spanning from 0.3 to 10 μm (Demanega et al., 2021;Kaur & Kelly, 2023), though laboratory studies have found that the Plantower PMS6003 and similar sensors have no ability to detect supermicron particles (Molina Rueda et al., 2023).

TEOM
The TEOM 1400a is a gravimetric PM monitor with the ability to make continuous mass measurements.It is one of the devices that has been designated as a Federal Equivalent Method by the United States Environmental Protection Agency.In principle, particle-laden air streams are drawn through a filter medium weighed in near real-time, usually every 2 s.The filter is placed on an elastic hollow glass-like tube (the tapered element), free on one end but clamped on the other, and set in constant oscillation by an electronic feedback system.This motion has a light-blocking effect on an LED-phototransistor pair and can be used to detect the frequency of oscillation of the element.As more particles are deposited on the filter, this frequency decreases and the changes are converted into a mass measurement (Ardon-Dryer et al., 2020;Kulkarni et al., 2011).
Changamwe, being an industrial area and home to the city's port activities, represents a hotspot for various industrial emissions.Vescon, situated in proximity to manufacturing and processing facilities, provides insights into the impact of industrial operations on air quality.Bamburi, with its mix of residential and industrial zones, serves as a representative sampling point for urban air quality.Nyali, a residential and tourist-centric area with scenic beaches, contributes information on air quality in areas frequented by residents and visitors.
The UoN site serves as a reference point, providing data on air quality in an educational and research setting.It houses the reference monitor (TEOM) and one of the low-cost sensors used in this study.The location at JKUAT has close proximity to the coastline and raises the possibility of sea spray contributing to local air quality dynamics.This is also true for Nyali found along the North coast of Mombasa.Each location offers a unique perspective on the challenges faced by Mombasa in maintaining air quality standards amid its economic and industrial activities.

Calibration Models
We collocated one Clarity Node-S with a reference-grade ThermoFisher TEOM 1400a installed at the UoN site from February to April 2023, spanning dry and wet months to fully account for seasonality.We compared the PM 2.5 data from these devices using a univariate regression model similar to Badura et al., 2019, a multiple linear regression (MLR), a Gaussian Mixture Regression (GMR), and a random forest (RF) model similar to approaches followed by Malings et al. (2019) and McFarlane, Isevulambire, et al. (2021), McFarlane, Raheja, et al. (2021).These methods have been commonly used due to their ease of use (especially linear regression), their accuracy, and their frequency of use in the literature.Other correction models such as extreme gradient boosting, neural networks, or other machine learning approaches have been used as well (Giordano et al., 2021).The best performing correction model with respect to the R 2 and mean absolute error (MAE) values was retrospectively applied to a five-sensor network in Mombasa that was operated between July 2021 and May 2022 to provide an overall survey of the air quality data in the city.

Correction of Low-Cost Sensor Measurements
Figure 2 shows the daily averaged Clarity Node-S PM 2.5 data initial correlation with reference grade (TEOM) PM 2.5 data with an R 2 value of 0.61 and initial mean absolute error (MAE = 7.03 μg m 3 ).
Including temperature and humidity data and modeling it using MLR, RF, and GMR models reduces the bias (Table 2).The MLR model had the best R 2 score of 0.61 and a reasonable MAE value of 4.28 μg m 3 .Further statistical evaluation is shown in Table S2 in Supporting Information S1.
Figure 3 shows the raw (purple), TEOM (olive), and corrected (red) hourly PM 2.5 data collected at the UoN site from February to April 2023.On most days, the temporal trend was reproduced and the sensors responded well to sudden spikes of mass concentrations.However, the raw and reference data were within 10 μg m 3 in the month of March but within 20 μg m 3 in February.In addition, the daily averaged raw data of the Clarity Nodes in most cases overpredicted the concentrations compared to reference grade TEOM monitor during the co-location period.

Daily PM 2.5 Measurements
Figure 4 summarizes the daily means of corrected PM 2.5 data from all six sites in a violin plot.Overall, the distributions are positively skewed mostly depicting a unimodal pattern and a majority of points between 10 and 20 μg m 3 .Some sites like Changamwe and Vescon have long-tail distributions compared to the rest, possibly alluding to heavy traffic or industrial activity experienced on some days.This is however not an exact intercomparison as different sites have different daily samples (indicated as N in the plots).According to the corrected plots, the highest daily PM 2.5 values are observed in Changamwe (42 μg m 3 ) while the lowest daily concentrations are observed in Nyali (4 μg m 3 ).The average concentrations are also the highest and lowest at these sites with Changamwe recording daily average of 16 μg m 3 respectively while Nyali has average of 11 μg m 3 respectively.Only the daily average of Changamwe exceeded the WHO daily PM 2.5 limit of 15 μg m 3 though there were days when this limit was exceeded in the other sites.

PM 2.5 Time Series Plot at Each Site
Figure 5 shows the temporal variations of corrected daily PM 2.5 concentrations from the six sites in Mombasa.Overall, the concentrations at each site exceeds the WHO annual guidelines of 5.0 μg m 3 in all days and exceeded the daily limit of 15.0 μg m 3 on only some days, ranging from 20% to 64% of days depending on the location (see pie charts in Figure 1).
Seasonal variations in PM 2.5 concentrations are evident with the highest monthly averages observed during the dry months (December to February) when the wet deposition is greatly reduced.This was followed by the cold months (July and August) where elevated PM 2.5 averages are also consistent with the lack of precipitation during this time period.By comparison, the lowest averages were in April and between October and November which correspond to the wet months where washout effect of the rain and wet deposition reduce the PM 2.5 levels.

Temporal Patterns in PM 2.5 Concentrations
The diurnal cycles, weekly, and daily variations of PM 2.5 in the six sites in Mombasa are presented in Figure 6.The highest PM 2.5 concentrations are most likely to appear on during weekends in a weekly cycle, and most  unlikely to appear on Thursdays.The large increases in tourist activity and consequently motor vehicles in the weekends are likely to be a reason leading to elevated PM 2.5 levels.
For five of the sites the diurnal cycles of PM 2.5 (top-left panel) displayed a bimodal pattern with early morning peaks between (6:00 a.m. and 8:00 a.m.) and afternoon peaks between (5.00 p.m. and 9:00 p.m.).This was  consistent with the increased anthropogenic activity caused by commuter travel habits during rush hour times and also by the changing mixing height.This is with exception to Changamwe whose morning and evening peaks came in much earlier than the other sites, most likely because of the activities at the port.During the rest of the day, traffic activities reduce and there is more mixing of pollutants hence a decrease in PM 2.5 concentrations.
One caveat of our study includes the retrospectively applied correction factor based on a single node air sensor and reference monitor co-location for only a few months.While best practice suggests a more robust co-location, similar approaches have been successfully applied in previous studies, especially in data sparse areas such as the African continent (McFarlane, Isevulambire, et al., 2021;Raheja et al., 2022).However, due to the timing of the colocation, we cover both a dry month (February) and wet months (March and April), thereby accounting for the predominant seasonality in the region.Additionally, concentrations during the colocation period months and the deployment months are similar in magnitude, suggesting that the co-location period was, to first order, an appropriate proxy for the deployment period.Finally, the co-location period had similar environmental conditions as the deployment period, as demonstrated in Table S1 in Supporting Information S1.

Conclusion and Recommendations
In conclusion, this study addresses the significant challenge of limited surface measurements of fine PM 2.5 in Sub-Saharan Africa, particularly in Mombasa, Kenya.The evaluation of Clarity Node-S PM sensors against a TEOM revealed moderate correlation and a MAE of approximately 7.03 μg m 3 in raw, manufacturer-reported data.
Through the application of calibration models, including MLR, GMR, and RF, the MAE was reduced to 4.28, 3.93, and 4.40 μg m 3 , respectively, with MLR achieving the highest R 2 value of 0.63.
Applying the correction factor to a five-sensor network in Mombasa provided valuable insights into the air quality, revealing average daily PM 2.5 concentrations ranging from 12 to 18 μg m 3 .Some sites, such as Changamwe, Vescon, and Bamburi, exceeded WHO daily PM 2.5 guidelines more than 50% of the time.Higher averages were observed during dry and cold seasons and during early morning and evening hours.
The study highlights the potential of low-cost sensor systems in regions with limited monitoring infrastructure, emphasizing their role in providing reliable air quality data where reference monitors are scarce.The findings contribute to the nascent field of air quality research in Mombasa, offering valuable information for future interventions and policies aimed at mitigating the health risks associated with air pollution.Though additional investigation is needed with larger networks, our first results suggest that PM 2.5 concentrations are moderately lower than other major African cities (e.g., Nairobi) (Pope et al., 2018).This could be attributed to many factors, likely including the close proximity to clean oceanic air masses owing to Mombasa's coastal location.The temporal and spatial variations in PM 2.5 concentrations underscore the need for continuous monitoring and targeted interventions to address air quality challenges in LMICs like Kenya.Future research should explore other areas within the city or other air pollutants not yet explored.Satellite data can also be used to map out potential hotspots followed by dedicated studies looking at the sources of pollution in the city.

Figure 1 .
Figure 1.A map of Mombasa and the deployment sites of the Clarity Nodes and the tapered element oscillating microbalance.The pie charts show the percentage of days where the concentration of PM 2.5 at each site exceeded (red) the WHO daily limit (15 μg m 3 ).

Figure 2 .
Figure 2. Performance evaluation and calibration of daily mean Clarity Node-S against tapered element oscillating microbalance-1400a PM 2.5 data.

Figure 3 .
Figure 3.A time series plot displaying the corrected, Clarity Node-S, and Tapered element oscillating microbalance-1400a PM 2.5 data.

Figure 4 .
Figure 4. Violin plots of daily averaged corrected PM 2.5 values for the entire data set at each location and six sites in Mombasa.

Figure 5 .
Figure 5. Timeseries plots of the daily PM 2.5 concentrations in six sites in Mombasa from July 2021 to May 2022.

Figure 6 .
Figure 6.Hourly average PM 2.5 concentrations of six sites in Mombasa organized into hour-of-day and day-of-week temporal trends.Shading represents the range.

Table 1
Sensor Deployment Locations in Mombasa

Table 2
The Statistical Performance Metrics of the Correction Models